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A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography
An Efficient Gradient-Free Simplex Method for Image-Guided Diffuse Optical Tomography
Automated Estimation Regularization Parameter for Diffuse Optical Tomography Using Minimal Residual Method
Back-projection type algorithm for Diffuse Optical Tomography: Open-Source Matlab Codes.
Calvin B. Shaw Thesis Work
Convolutional neural networks based robust Denoising of Low-Dose Computed Tomography Perfusion maps
Deep neural network for bandwidth enhancement of photoacoustic data
Deep Neural-Network Based Sinogram Super-resolution and Bandwidth Enhancement for Limited Data Photoacoustic Tomography
Dimensionality Reduced Plug and Play Priors for Improving Limited data Photoacoustic Tomography
Comparison of iterative parameteric and indirect DL based recon method
Direct Sensitivity Matrix
dweenprograms
Fast and exact modeling of acoustic wavefield for photoacoustic imaging
General purpose GPU has limited utility compared to traditional multi-core CPU computing in diffuse optical tomography
Helmholtz of Gaussian
Image Guided Filtering for Improving Photoacoustic Tomographic Image Reconstruction
Improved Quantitative Susceptibility Mapping with Weighted Total Least Squares
Incoherence based optimal selection of independent measurements in diffuse optical tomography
Jaya Prakash PhD Thesis
Kalyan M.Sc Thesis
MATLAB Codes for Calvin's M.Sc. [Engg] Thesis
Matlab Programs for Jayaprakash's Thesis
Minimal Residual Method based optimal selection of Regularization Parameter in Image Restoration
Model-Resolution based Basis Pursuit Deconvolution Improves Diffuse Optical Tomographic Imaging
Modeling errors compensation with total least squares for photoacoustic tomography
Performance evaluation of typical approximation algorithms for non-convex Lp-minimization in diffuse optical tomography
R-net: A Deep Convolutional Neural Network for Improving Photoacoustic Image Reconstruction
Ravi Prasad K. J. Thesis Work
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography
Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction
Vector Extrapolation Methods for Accelerating Iterative Reconstruction Methods in Limited-Data Photoacoustic Tomography
Estimation of optimal regularization parameter using Morozov discrepancy principle for DOT
Manish's PhD thesis work
Spatially Varying Regularization for Photoacoustic Tomography
sercmig
Estimation of optimal regularization parameter using Morozov discrepancy principle for DOT
Matlab Codes* :
(
requires
NIRFAST
)
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